Clicking the tabs from left to right

It looks like visitors to the Resolver Systems website are predisposed to clicking through the tabs at the top of the page, from left to right. Does anyone else see this kind of thing?

The figures I’m using are from Google Analytics, which is based on JavaScript embedded in the page and run in the browser, so I don’t think it’s caused by bots/crawlers just clicking each of the tabs in turn because they appear in the page source code in that order — and in addition, if it were a result of automated systems, you’d expect a consistent bias, whereas it’s actually quite variable.

Here’s the full dataset. Each line below shows a Google Analytics overlay, which tells you for each selected tab what percentage of people clicked on each of the other tabs during July 2009:

It looks like we managed to break tracking of access to our “About us” page for that month, so I put the results for that tab aside and did a bit of simple statistical analysis (in Resolver One, naturally) on the remaining data. The results:

The “from” tab is top to bottom, the “to” tab is left to right — so, the chance of someone who is currently on the “Buy” tab clicking “Download” is 29%. The average chance of someone clicking on a given tab across all sources, and the standard deviation of those figures, are summarised at the bottom. Each cell is coloured based on how many standard deviations from the average it lies — if it’s more popular than it normally is, it appears red, if it’s less popular it’s green. The intensity of the red/green is based on how much more/less popular it is.

I think there’s a very clear pattern — the line of red starting at the “Home tab to Buy tab” cell, and going down and to the right to the “Screencasts tab to Get help tab” cell. That indicates that people are significantly more likely to click on a tab when it’s the one to the right of the one they’re currently looking at.

3 thoughts on “Clicking the tabs from left to right”

One idea comes to mind, basically it looks like Google Ad sense model. you can model your website as a Markov Chain, you have already calculated the elements of your transition matrix, now you can also calculate your stationary probability for each state (each state being one link).

In Google Ad sense the stationary probability of each state determines the cost of placing an advertisement on each state (site/link) the higher the stationary probability the higher the user visits to that state and the higher the cost of an ad. By looking at the whole thing as a Markov chain I believe one can infer many interesting results. perhaps you can calculate the amount of time each visitor stays on a page and model it better with a continuous time MC where users stay on a page according to a Poisson process. I know Google uses these models efficiently on huge transition matrices. though I am not sure if these models will be as helpful on individual websites.